



import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax

							from transformers.models.big_bird.modeling_flax_big_bird import (
							    FlaxBigBirdForCausalLM,
							    FlaxBigBirdForMaskedLM,
							    FlaxBigBirdForMultipleChoice,
							    FlaxBigBirdForPreTraining,
							    FlaxBigBirdForQuestionAnswering,
							    FlaxBigBirdForSequenceClassification,
							    FlaxBigBirdForTokenClassification,
							    FlaxBigBirdModel,
							)
class 					_snake_case      (       unittest.TestCase):




				def __init__(    self       :    List[Any],       __lowercase       :    Union[str, Any],       __lowercase       :    Union[str, Any]=2,       __lowercase       :    Dict=56,       __lowercase       :    str=True,       __lowercase       :    Optional[Any]=True,       __lowercase       :    List[Any]=True,       __lowercase       :    Union[str, Any]=True,       __lowercase       :    Any=99,       __lowercase       :    Optional[int]=32,       __lowercase       :    Tuple=2,       __lowercase       :    int=2,       __lowercase       :    List[str]=7,       __lowercase       :    Any="gelu_new",       __lowercase       :    List[str]=0.1,       __lowercase       :    str=0.1,       __lowercase       :    List[Any]=512,       __lowercase       :    List[str]=16,       __lowercase       :    Optional[int]=2,       __lowercase       :    Union[str, Any]=0.02,       __lowercase       :    Union[str, Any]=4,       __lowercase       :    Union[str, Any]="block_sparse",       __lowercase       :    Tuple=True,       __lowercase       :    Dict=False,       __lowercase       :    Dict=2,       __lowercase       :    Dict=3,       ):
							lowercase__       	=					parent
							lowercase__       	=					batch_size
							lowercase__       	=					seq_length
							lowercase__       	=					is_training
							lowercase__       	=					use_attention_mask
							lowercase__       	=					use_token_type_ids
							lowercase__       	=					use_labels
							lowercase__       	=					vocab_size
							lowercase__       	=					hidden_size
							lowercase__       	=					num_hidden_layers
							lowercase__       	=					num_attention_heads
							lowercase__       	=					intermediate_size
							lowercase__       	=					hidden_act
							lowercase__       	=					hidden_dropout_prob
							lowercase__       	=					attention_probs_dropout_prob
							lowercase__       	=					max_position_embeddings
							lowercase__       	=					type_vocab_size
							lowercase__       	=					type_sequence_label_size
							lowercase__       	=					initializer_range
							lowercase__       	=					num_choices

							lowercase__       	=					rescale_embeddings
							lowercase__       	=					attention_type
							lowercase__       	=					use_bias
							lowercase__       	=					block_size
							lowercase__       	=					num_random_blocks




				def 				A__   (    self       :    List[Any]    ):
							lowercase__       	=					ids_tensor([self.batch_size, self.seq_length],       self.vocab_size    )

							lowercase__       	=					None
							if self.use_attention_mask:
										lowercase__       	=					random_attention_mask([self.batch_size, self.seq_length]    )

							lowercase__       	=					None
							if self.use_token_type_ids:
										lowercase__       	=					ids_tensor([self.batch_size, self.seq_length],       self.type_vocab_size    )

							lowercase__       	=					BigBirdConfig(
							    vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       num_hidden_layers=self.num_hidden_layers,       num_attention_heads=self.num_attention_heads,       intermediate_size=self.intermediate_size,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       max_position_embeddings=self.max_position_embeddings,       type_vocab_size=self.type_vocab_size,       is_decoder=lowercase_,       initializer_range=self.initializer_range,       attention_type=self.attention_type,       block_size=self.block_size,       num_random_blocks=self.num_random_blocks,       use_bias=self.use_bias,       rescale_embeddings=self.rescale_embeddings,       )

							return config, input_ids, token_type_ids, attention_mask




				def 				A__   (    self       :    Optional[Any]    ):
							lowercase__       	=					self.prepare_config_and_inputs()
							lowercase__	,							lowercase__	,							lowercase__	,							lowercase__       	=					config_and_inputs
							lowercase__       	=					{
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "attention_mask": attention_mask,
							}
							return config, inputs_dict
@require_flax
class 					_snake_case      (       lowercase__     ,						unittest.TestCase):
				UpperCamelCase__					:       List[str]  		=(
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				UpperCamelCase__					:       List[Any]  		=False
				UpperCamelCase__					:       Optional[Any]  		=False




				def 				A__   (    self       :    Optional[int]    ):
							lowercase__       	=					FlaxBigBirdModelTester(self    )




				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 				A__   (    self       :    Union[str, Any]    ):
							super().test_from_pretrained_save_pretrained()




				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 				A__   (    self       :    Any    ):
							super().test_from_pretrained_with_no_automatic_init()




				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 				A__   (    self       :    List[Any]    ):
							super().test_no_automatic_init()




				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 				A__   (    self       :    str    ):
							super().test_hidden_states_output()




				@slow
				def 				A__   (    self       :    Tuple    ):







							for model_class_name in self.all_model_classes:
										lowercase__       	=					model_class_name.from_pretrained("google/bigbird-roberta-base"    )
										self.assertIsNotNone(lowercase_    )




				def 				A__   (    self       :    Tuple    ):







							if self.test_attn_probs:
										super().test_attention_outputs()




				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 				A__   (    self       :    Tuple    ):
							lowercase__	,							lowercase__       	=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__    ):
													lowercase__       	=					self._prepare_for_class(lowercase_,       lowercase_    )
													lowercase__       	=					model_class(lowercase_    )

													@jax.jit
													def model_jitted(__lowercase       :    Dict,       __lowercase       :    List[Any]=None,       **__lowercase       :    Tuple    ):
																return model(input_ids=lowercase_,       attention_mask=lowercase_,       **lowercase_    )

													with self.subTest("JIT Enabled"    ):
																lowercase__       	=					model_jitted(**lowercase_    ).to_tuple()

													with self.subTest("JIT Disabled"    ):
																with jax.disable_jit():
																			lowercase__       	=					model_jitted(**lowercase_    ).to_tuple()

													self.assertEqual(len(lowercase_    ),       len(lowercase_    )    )
													for jitted_output, output in zip(lowercase_,       lowercase_    ):
																self.assertEqual(jitted_output.shape,       output.shape    )




				def 				A__   (    self       :    Any,       __lowercase       :    List[str],       __lowercase       :    List[str],       __lowercase       :    List[str],       __lowercase       :    str=1e-5,       __lowercase       :    int="outputs",       __lowercase       :    List[str]=None    ):







							if name.startswith("outputs.attentions"    ):
										return
							else:
										super().check_pt_flax_outputs(lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_    )





from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()




